Opensky

OpenSky provides api to get data for flights in progress. It is rate limited

Code

import requests
import pandas as pd
import geopandas

Code

def fetch_opensky_data():
    url = "https://opensky-network.org/api/states/all"
    response = requests.get(url)
    data = response.json()

    columns = [
        "icao24", "callsign", "origin_country", "time_position", "last_contact",
        "longitude", "latitude", "baro_altitude", "on_ground", "velocity",
        "heading", "vertical_rate", "sensors", "geo_altitude", "squawk",
        "spi", "position_source"
    ]

    df_raw = pd.DataFrame(data['states'], columns=columns)
    return (
        df_raw
        .assign(
            time_position=pd.to_datetime(df_raw["time_position"], unit="s"),
            last_contact=pd.to_datetime(df_raw["last_contact"], unit="s"),
        )
    )

df_flights = fetch_opensky_data()


df_flihts_non_nan = df_flights.dropna(subset=["longitude", "latitude"])
gdf = geopandas.GeoDataFrame(
    df_flihts_non_nan,
    geometry=geopandas.points_from_xy(df_flihts_non_nan.longitude, df_flihts_non_nan.latitude),
    crs="EPSG:4326",
)

gdf.explore(fullscreen=True, tooltip=False, popup=True)

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Flights in world at notebook time

Number of flights by origin_country

Code

(
    df_flights
    .groupby("origin_country")
    .agg(num_flights=("icao24", "count"))
    .sort_values("num_flights", ascending=False)
    .head(20)
)

	num_flights
origin_country
United States	4669
United Kingdom	340
Canada	325
Ireland	228
Turkey	140
Germany	124
China	118
United Arab Emirates	113
Australia	106
Malta	104
Spain	102
Brazil	91
India	88
Kingdom of the Netherlands	82
France	76
Mexico	65
Austria	61
Japan	58
New Zealand	56
Sweden	54

Top 20 countries with most flights

All flights

For reference

Code

df_flights

	icao24	callsign	origin_country	time_position	last_contact	longitude	latitude	baro_altitude	on_ground	velocity	heading	vertical_rate	sensors	geo_altitude	squawk	spi	position_source
0	ab1644	UAL8191	United States	2025-05-17 21:23:51	2025-05-17 21:23:52	-77.9301	35.3311	11262.36	False	198.84	357.03	0.33	None	11711.94	7046	False	0
1	a1abf5	N2069W	United States	2025-05-17 21:23:15	2025-05-17 21:23:15	-90.5551	14.5664	1653.54	False	69.18	312.59	3.58	None	1729.74	1601	False	0
2	e8027d	LPE2454	Chile	2025-05-17 21:19:15	2025-05-17 21:19:19	-86.9373	21.0897	381.00	False	72.06	123.35	-3.58	None	396.24	None	False	0
3	39de4a	TVF86LY	France	2025-05-17 21:23:52	2025-05-17 21:23:52	-7.8084	35.5199	11879.58	False	208.47	178.02	-0.33	None	12207.24	7644	False	0
4	801641	AXB698	India	2025-05-17 21:23:52	2025-05-17 21:23:52	53.1650	25.1998	10668.00	False	241.39	121.05	0.00	None	11338.56	None	False	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7872	7c4807	OIH	Australia	2025-05-17 21:23:52	2025-05-17 21:23:52	153.5465	-28.1436	1767.84	False	84.43	333.59	-12.03	None	1866.90	1733	False	0
7873	4caa32	ITY619	Ireland	2025-05-17 21:23:51	2025-05-17 21:23:51	-77.4537	38.9467	NaN	True	5.40	270.00	NaN	None	NaN	None	False	0
7874	458664	CAT750	Denmark	2025-05-17 21:23:52	2025-05-17 21:23:52	21.8020	45.8280	11582.40	False	233.05	330.95	0.00	None	11551.92	None	False	0
7875	458666	CAT9752	Denmark	2025-05-17 21:23:52	2025-05-17 21:23:52	15.7253	52.3409	12192.00	False	230.90	327.37	0.00	None	12123.42	4532	False	0
7876	458665	CAT314	Denmark	2025-05-17 21:23:52	2025-05-17 21:23:52	6.4021	46.4385	11582.40	False	201.63	350.01	0.00	None	11719.56	5334	False	0

7877 rows × 17 columns